Remote Sensing and Modeling (RSM) Lab

I am an Assistant Professor in Kimbell School of Geosciences and the primary investigator of "Remote Sensing and Modeling (RSM)" lab at Midwestern State University Texas. 

I worked as a Research Scientist in the Department of Geography at Indiana University Bloomington from September 2019 to June 2021. I worked in MacBean Lab to understand the response of the carbon cycle and terrestrial ecosystems to climate and environmental changes. I was using data assimilation methods to constrain global carbon cycle sink projections and modeled carbon-climate feedback. I used a wide array of observations to test, develop and constrain terrestrial biosphere models that form the land component of earth system models. I was also performing geospatial modeling using both satellite and airborne remote sensing data to understand topographic and environmental drivers of vegetation distribution.

I was a postdoctoral research fellow at the Hawkesbury Institute for the Environment in Western Sydney University from February 2016 to June 2019. During this time, I was responsible for the hands-on implementation of the ARC Discovery project 'Modelling Optimal Plant Behaviour, or Do Plants Hedge Their Bets?' I have worked in collaboration with Professor Belinda Medlyn (WSU), Dr Remko Duursma (WSU), Professor Roderick Dewar (ANU) and Professor Mat Williams (Edinburgh University) to develop the Data Assimilation (DA) framework and implementation of numerical optimization routines. 

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I received PhD from the school of Civil and Environmental Engineering at UNSW Australia in November 2015. I have been also granted a three months Post-Doctoral Writing Fellowship from UNSW Faculty of Engineering. During three and a half years of research as a post graduate student at UNSW Australia, I have performed intensive research and analysed information about complex hydrogeological behaviour of aquifer including heterogeneity, uncertainty, connectivity, flow and transport processes. The main objectives of this research are two-fold: firstly to develop a Training Image (TI) based approach for stochastic simulations with the application in surface and subsurface processes, and secondly to present the first quantitative analysis of the morphology and spatial distribution of cave ceiling features based on LiDAR data to identify flow processes and estimate groundwater recharge through subsurface karst formations.